Brain Corporation

Borja Ibarz Gabardos

Jean-Baptiste Passot

Oleg Sinyavskiy

Eugene Izhikevich

Patryk Laurent

Filip Ponulak

Peter O'Connor

Diana Vu Le

Philip Meier

Andrew Nolan Smith

Jaldert Rombouts

Botond Szatmary

Cody Griffin

Vadim Polonichko

Jayram Moorkanikara Nageswaran

Andrew Smith

Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Systems and methods for training a robot to autonomously travel a route

Systems and methods for robotic path planning are disclosed. In some implementations of the present disclosure, a robot can generate a cost map associated with an environment of the robot. The cost map can comprise a plurality of pixels each corresponding to a location in the environment, where each pixel can have an associated cost. The robot can further generate a plurality of masks having projected path portions for the travel of the robot within the environment, where each mask comprises a plurality of mask pixels that correspond to locations in the environment. The robot can then determine a mask cost associated with each mask based at least in part on the cost map and select a mask based at least in part on the mask cost. Based on the projected path portions within the selected mask, the robot can navigate a space.

Systems and methods for robotic path planning

Systems and methods assisting a robotic apparatus are disclosed. In some exemplary implementations, a robot can encounter situations where the robot cannot proceed and/or does not know with a high degree of certainty it can proceed. Accordingly, the robot can determine that it has encountered an error and/or assist event. In some exemplary implementations, the robot can receive assistance from an operator and/or attempt to resolve the issue itself. In some cases, the robot can be configured to delay actions in order to allow resolution of the error and/or assist event.

Systems and methods for assisting a robotic apparatus

Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.

Systems and methods for robotic behavior around moving bodies

Systems and methods for robotic mapping are disclosed. In some example implementations, an automated device can travel in an environment. From travelling in the environment, the automated device can create a graph comprising a plurality of nodes, wherein each node corresponds to a scan taken by one or more sensors of the automated device at a location in the environment. In some example embodiments, the automated device can reevaluate its travel along a desired path if it encounters objects or obstructions along its path, whether those objects or obstructions are present in the front, rare or side of the automated device. In some example embodiments, the automated device uses a timestamp methodology to maneuver around its environment that provides faster processing and less usage of memory space.

Systems and methods for robust robotic mapping

Robotic devices may be trained by a user guiding the robot along target action trajectory using an input signal. A robotic device may comprise an adaptive controller configured to generate control signal based on one or more of the user guidance, sensory input, performance measure, and/or other information. Training may comprise a plurality of trials, wherein for a given context the user and the robot's controller may collaborate to develop an association between the context and the target action. Upon developing the association, the adaptive controller may be capable of generating the control signal and/or an action indication prior and/or in lieu of user input. The predictive control functionality attained by the controller may enable autonomous operation of robotic devices obviating a need for continuing user guidance.

Predictive robotic controller apparatus and methods

The safe operation and navigation of robots is an active research topic for many real-world applications, such as the automation of large industrial equipment. This technological field often requires heavy machines with arbitrary shapes to navigate very close to obstacles, a challenging and largely unsolved problem. To address this issue, a new planning architecture is developed that allows wheeled vehicles to navigate safely and without human supervision in cluttered environments. The inventive methods and systems disclosed herein belong to the Model Predictive Control (MPC) family of local planning algorithms. The technological features disclosed herein works in the space of two-dimensional (2D) occupancy grids and plans in motor command space using a black box forward model for state inference. Compared to the conventional methods and systems, the inventive methods and systems disclosed herein include several properties that make it scalable and applicable to a production environment. The inventive concepts disclosed herein are at least deterministic, computationally efficient, run in constant time and can be deployed in many common non-holonomic systems.

Systems and methods for precise navigation of autonomous devices

Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.

Apparatus and methods for control of robot actions based on corrective user inputs

Systems and methods for robotic mapping are disclosed. In some exemplary implementations, a robot can travel in an environment. From travelling in the environment, the robot can create a graph comprising a plurality of nodes, wherein each node corresponds to a scan taken by a sensor of the robot at a location in the environment. In some exemplary implementations, the robot can generate a map of the environment from the graph. In some cases, to facilitate map generation, the robot can constrain the graph to start and end at a substantially similar location. The robot can also perform scan matching on extended scan groups, determined from identifying overlap between scans, to further determine the location of features in a map.

Systems and methods for robotic mapping

Systems and methods for dynamic route planning m autonomous navigation are disclosed. In some exemplary implementations, a robot can have one or more sensors configured to collect data about an environment including detected points on one or more objects in the environment. The robot can then plan a route in the environment, where the route can comprise one or more route poses. The route poses can include a footprint indicative at least in part of a pose, size, and shape of the robot along the route. Each route pose can have a plurality of points therein. Based on forces exerted on the points of each route pose by other route poses, objects in the environment, and others, each route pose can reposition. Based at least in part on interpolation performed on the route poses (some of which may be repositioned), the robot can dynamically route.

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La Jolla, CA, United States of America

Borja Ibarz Gabardos